A survey on Predictive Analysis in Employment Trends
About this article
DOI:
https://doi.org/10.14419/ijet.v7i2.24.12082Keywords:
Predictive Analysis, Employability, Data Mining.Abstract
This paper addresses the theories of using predictive analysis and Data Mining in arriving at suitable patterns and predicting paths and trends in the current Employment Scenario more specifically to the Engineering sector. India produces 1.5 million engineers every year, and yet there is a significant gap between their skills and the jobs and corresponding salaries they are offered. Recognizing the factors that influence this gap can help us bridge it. The survey shows that the ideal route to doing so, is by employing various Predictive analysis and Data Mining techniques on appropriate data sets, which help in addressing these issues. As per the survey, appropriate visualization techniques have also been used to extract the meaning from the prediction and analysis.
References
“The skills gap and what it means for your business,” www.financialexpress.com/article/industry/jobs/the-skills-gap-andwhat-it-means-for-your-business/138700, accessed: 2016-01-29.
“Predictive Analysis and Data Mining among the Employment of Fresh Graduate Students in HEI”
Nor Azziaty Abdul Rahman, Kian Lam Tan, Chen Kim Lim, https://doi.org/10.1063/1.5005340
Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 33–42. https://doi.org/10.1007/s11761- 012-0122-2
Mishra, T. (2016). Students’ Employability Prediction Model through Data Mining, 11(4), 2275–2282.
View more references (11)
Sapaat, M. A., Mustapha, A., Ahmad, J., &Chamili, K. (2011). A Data Mining Approach to Construct Graduates Employability Model in Malaysia, 1(4), 1086–1098
Job and Candidate Recommendation with Big Data Support: A Contextual Online Learning Approach., GLOBECOM 2017 - 2017 IEEE Global Communications Conference
Xianyin Li, Wanming Chen (2009). A Grey –Markov Predication for unemployment rate of graduates in China
Aziz, A. A., Ismail, N. H., Ahmad, F., & Hassan, H. (2015). A framework for students’ academic performance analysis using naive bayes classifier. JurnalTeknologi, 75(3), 13–19. https://doi.org/10.11113/jt.v75.5037
Student Academic Performance and Social Behavior Predictor using Data Mining Techniques, Computing, Communication and Automation (ICCCA), 2017 International Conference on
Gao, L. (2015). Analysis of Employment Data Mining for University Student based on Weka Platform, 2(4), 130–133
Jantawan, B., & Tsai, C. (2013). The Application of Data Mining to Build Classification Model for Predicting Graduate Employment. International Journal of Computer Science and Information Security, 11(10), 1–8. https://doi.org/10.1016/j.bdr. 2015.01.001
Chih-Chou Chiu' and Chao-Ton Su2. (2014). A Novel Neural Network Model Using Box-Jenkins Technique and Response Surface Methodology to Predict Unemployment Rate
Dr. Yashpal Singh, Alok Singh Chauhan, Neural Networks in Data Mining.
T. Padmapriya and V. Saminadan, “Inter-cell Load Balancing Technique for Multi- class Traffic in MIMO - LTE - A Networks”, International Conference on Advanced Computer Science and Information Technology , Singapore, vol.3, no.8, July 2015.
S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015, Publisher: IEEE,DOI:10.1109/ECS.2015.7124833.